Energy-Efficient Reservoir Computing Based on Solution-Processed Electrolyte/Ferroelectric Memcapacitive Synapses for Biosignal Classification

The classification of critical physiological signals using neuromorphic devices is essential for early disease detection. Physical reservoir computing (RC), a lightweight temporal processing neural network, offers a promising solution for low-power, resource-constrained hardware. Although solution-processed memcapacitive reservoirs have the potential to improve power efficiency as a result of their ultralow static power consumption, further advancements in synaptic tunability and reservoir states are imperative to enhance the capabilities of RC systems. This work presents solution-processed electrolyte/ferroelectric memcapacitive synapses. Leveraging the synergistic coupling of electrical double-layer (EDL) effects and ferroelectric polarization, these synapses exhibit tunable long- and short-term plasticity, ultralow power consumption (∼27 fJ per spike), and rich reservoir state dynamics, making them well-suited for energy-efficient RC systems. The classifications of critical electrocardiogram (ECG) signals, including arrhythmia and obstructive sleep apnea (OSA), are performed using the synapse-based RC system, demonstrating excellent accuracies of 97.8 and 80.0% for arrhythmia and OSA classifications, respectively. These findings pave the way for developing lightweight, energy-efficient machine-learning platforms for biosignal classification in wearable devices.